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Experience
Roles and systems I’ve owned end-to-end, with a focus on AI agents, n8n workflow architecture, and automation reliability.
Founding Member | AI Automation Engineer (AI Agents & n8n)
Obby (U.S.-Based Marketing Agency) | Part Time (Remote)
Jan 2023 – Present
Built the agency’s AI automation infrastructure from the ground up, using AI Agents and n8n to standardize how leads, campaigns, reporting, and internal operations move across systems. Focused on reliability, observability, and measurable business outcomes.
- Designed and deployed AI Agents for lead qualification, data normalization, routing decisions, and automated follow-ups inside n8n workflows.
- Architected and maintained 500+ workflows across CRMs, ad platforms, lead sources, and communication tools, improving speed-to-lead and data accuracy.
- Built AI-assisted lead routing systems that reduce human decision-making while preserving control, auditability, and consistent outcomes.
- Automated CRM lifecycle workflows in GoHighLevel, including pipeline updates, AI-driven task creation, and SMS/email triggers.
- Implemented monitoring, logging, and alerting so automation failures are detected early before impacting operations.
Tools & Technologies
- AI Agents (LLM-based decision systems)
- n8n (workflow orchestration, webhooks, JS Code nodes)
- GoHighLevel (workflows, pipelines, triggers)
- Zapier, Make (Integromat)
- Google Apps Script, Google Sheets API
- Meta Ads API, Google Ads automation
- Slack, Twilio
- Python, Node.js, REST APIs
- SQL, Firebase, Data Modeling
Programming Languages
- JavaScript / Node.js
- Python
- TypeScript
- C/C++
- SQL
AI Trainer
Remotasks | Contract (Remote)
Jan 2024 – Mar 2024
Worked as an AI Trainer contributing to the development, evaluation, and improvement of large language model outputs through structured data labeling, reasoning validation, and quality control.
- Reviewed and corrected AI-generated outputs with a focus on logical consistency, correctness, and instruction adherence.
- Performed detailed reasoning validation and edge-case analysis to improve model reliability.
- Followed strict quality, accuracy, and safety guidelines while working on production AI training tasks.
- Developed a strong understanding of how LLMs interpret instructions, handle ambiguity, and fail under complex scenarios.
Tools & Technologies
- LLM evaluation frameworks
- Structured data labeling systems
- Quality assurance and review pipelines
Programming Languages
- English
- Python (reasoning analysis)